Echo based room estimation

ABSTRACT

A method for estimating an acoustic influence of walls of a room, comprising emitting a known excitation sound signal, receiving a set of measurement signals, each measurement signal being received by one microphone in a microphone array and each measurement signal including a set of echoes caused by reflections by the walls, solving a linear system of equations to identify locations of image source and estimating the acoustic influence based these image sources. The signal model includes a convolution of:
         the excitation signal,   a multichannel filter (M) representing the relative delays of the microphones in the microphone array, the relative delays determined based on a known geometry of the microphone array, and   a directivity model ν(n, p) of the driver(s) in the form of an anechoic far-field impulse response as a function of transmit angle.

CROSS-REFERENCE TO RELATED APPLICATION

This patent U.S. patent application claims priority to and the benefitof European Application No. 19203830.5 filed Oct. 17, 2019. The entirecontent of this application is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to estimation of the influence of a room(and specifically the walls of the room) on an acoustic experience. Theestimation is based on received echoes from transmitted sound signals.

BACKGROUND OF THE INVENTION

More advanced loudspeaker systems make it possible to control andpersonalize the listening experience by advanced sound control. Examplesinclude personalized listening zones, sound field navigation,object-based audio, surround audio, echo-free teleconferencing, activeroom compensation, etc.

Many such audio control techniques require at least some prior knowledgeof the acoustic properties of the surrounding space, e.g. the room andhow they will influence the sound. For example, reflections caused bythe walls of the room may significantly impact the listening experience.It is therefore desirable that a loudspeaker system (e.g. a hi-fi stereosystem, a soundbar of a multimedia playback system, or ateleconferencing system) automatically can determine the loudspeakers'position in relation to at least some of the walls of the room, and howthese walls will influence the sound.

Recently, intelligent loudspeakers that are equipped with inexpensivemicrophones have entered the consumer market. These systems usuallyconsist of an enclosure with multiple loudspeaker drivers and amicrophone array. With the microphone array, the system is capable ofrecognizing speech commands from users which has created a new interfacebetween human and machine. It has been proposed to use such microphonesto also determine the location of the room walls.

The general principle is that when a loudspeaker emits a sound signalinto the room, it will be reflected (echoed) by the walls. Themicrophones will receive these echoes in the form of delayed versions ofthe transmitted signal (filtered by the loudspeaker, walls, andmicrophones). The direct path contribution (i.e. the emitted signalreceived directly by the microphones, without reflection from the walls)is typically known (since the relative position between loudspeaker andmicrophones is known and constant over time) and can therefore beeliminated. Distances to the walls can then be determined by estimatingthe precise delay of the echoes from the walls, and by using therelative delay between microphones in the array to determine angles.

In practice, such echo detection is rather challenging, as the echoes ofthe transmitted signal are concealed by the filtering of theloudspeaker, walls, and microphones.

A general approach to handling this challenge is provided in FIG. 1 ,where it is assumed that the source (loudspeaker) and receiver(microphone array) are synchronized and that the excitation signal(emitted signal) is known (but not necessarily predetermined).

First, in step 1, the known excitation signal is deconvolved from thereceived signal, to obtain an estimated channel. Then, in step 2, aloudspeaker model is used to remove the influence of the loudspeakerresponse on the channel estimate. The result is an estimation of theroom impulse response. Next, in step 3, echoes are identified in theimpulse response, and in step 4 the echoes are assigned to possiblewalls, and the location of the walls is determined using the knowngeometry of the microphone array. It is noted that the four steps arenot necessarily performed as discrete steps.

There are various prior art examples following this general approach,but differing in setup and assumptions. Some useful assumptions(simplifications) are:

-   -   1. The emitted and received acoustic signals (sound fields) are        modeled as “sound rays” which propagate in straight narrow lines        and is reflected specularly by the walls.    -   2. The microphone array is compact with respect to the distance        to the walls (i.e. array diameter«distance to walls). This        allows plane wave assumption for all echoes.

As an example of prior art solutions, reference is made to US2011/0317522, which generally follows the approach in FIG. 1 , althoughsome of the steps have been combined and handled jointly. Specifically,the excitation signal is deconvolved from the measurement signals beforeany estimation takes place. Although in principle any known excitationsignal may be deconvolved from the measured signals, significantinformation will be lost unless the excitation signal complies with somerather strict requirements. Specifically, the excitation signal ispreferably a wide band, non-periodic signal. In US 2011/0317522, alinear sine sweep from 30 Hz to 8 kHz is given as an example.

Further, the method in US 2011/0317522 performs step 3 in FIG. 1 bymatching the measured signals with an empiric model based on apre-stored dataset including a large number of measurements ofreflections against a single wall in an anechoic chamber. Each suchmeasurement represents a “single wall impulse response” (SWIR). As areflection involves both loudspeaker (transmission), wall (reflection)and microphones (reception), the measurements and the resulting modelwill be specific to the actual setup used in the measurement. In otherwords, the measurements must be made using the same microphone arraythat will be used in the end product, and with a wall which has the same(or at least similar) properties as the walls of the room where the endproduct is used.

Another example of a prior art solution following a similar approach isprovided by the article “Geometrically Constrained Room Modeling WithCompact Microphone Array”, but Ribeiro et al., IEEE Transactions onAudio, Speech and Language Processing, Vol. 20, No 5, July 2012. Also inthis case, the model is based on wall impulse responses (WIR) which arecollected in an anechoic chamber using a barrier as wall simulator (seepage 1455, left col., middle). Further, and similar to US 2011/0317522,the excitation signal is deconvolved from the measurement signals beforeany estimation takes place. Specifically, on page 1452, left column, endof section A, it is stated that the room impulse response (RIR) isestimated from observations y_(m)(n), given a persistent exciting signals(n). Again, therefore, the performance of the model will depend on thecharacteristics of the excitation signal.

It would be desirable to provide a more versatile approach, lessrestricted by the excitation signal and requiring a less demandingmodelling process.

GENERAL DISCLOSURE OF THE INVENTION

This and other objects are achieved by method for estimating an acousticinfluence of walls of a room, using a system including a loudspeakerincluding at least one acoustic driver, and a compact microphone arrayincluding a set of microphones arranged in a known geometry around theloudspeaker.

The method comprises emitting a known excitation sound signal, receivinga set of measurement signals, each measurement signal being received byone microphone in the microphone array and each measurement signalincluding a direct path component and a set of echoes, the echoes causedby reflections by the walls, defining a linear system of equations y=Φh,wherein y is the set of measurement signals, Φ is a signal model of thesystem, and h is a vector, each element of h representing a candidatelocation of an image source with a value representing a gain of theimage source, identifying non-zero values of h by using least squareestimation to minimize |y−Φh|, wherein the least-squares estimation is

₁-regularized in order to restrict the number of non-zero values of h,and estimating the acoustic influence based on image sourcescorresponding to the identified non-zero values of h. The signal modelincludes a convolution of:

-   -   the excitation signal,    -   a multichannel filter representing the relative delays of the        microphones in the microphone array, the relative delays        determined based on a known geometry of the microphone array,        and    -   a directivity model of the driver(s) in the form of an anechoic        far-field impulse response as a function of transmit angle.

By “walls” is here intended any large, generally flat, verticalreflecting surface in the room. A person skilled in the art will realizethat the method according to the invention may be generalized to alsoinclude reflections also from non-vertical surfaces, such as from theceiling.

By “acoustic driver” is generally intended an electroacoustic driver,although other types of driver, capable of generating sound waves, arefeasible.

A “compact” microphone array refers to an array having an maximumextension (maximum distance between two microphones) is significantly,e.g. 5 times, smaller than the distance from the microphone array to thewalls.

Contrary to US 2011/0317522 and also to the approach in the article byRibeiro et al, the excitation signal is included in the signal model. Asa consequence, no “pre-processing” in the form of deconvolving themeasurement signals with the excitation signal is required (step 1 inFIG. 1 ). This means that no information from the excitation signal islost in an initial deconvolution step. As the present invention thusincludes all available information, it becomes more robust.

Further, the relative delay of the microphones is analytically ornumerically modeled by the multichannel filter, based on a knowngeometry of the microphone array. Such relative delays will not dependon the type of microphones (their frequency response) and this doestherefore not need to be measured beforehand. The only information thatneeds to be empirically determined is the directivity of the loudspeakerdriver(s), which is modeled by a series of far-field impulse responsesmeasured in an anechoic environment.

In other words, the solution according to the present invention does notrely on measurements of wall impulse responses, which is done in US2011/0317522 as well as in the article by Ribeiro et al. By avoiding toinclude microphones and a wall in the empiric model, the approachaccording to the invention will not be restricted to a specificmicrophone type nor to specific wall properties.

Preferably, the center of the microphone array is at, or close to, apoint source representing the loudspeaker. In the case of severaldrivers, there is no single point source. In this case it isadvantageous if all drivers (all point sources) are close to the centerof the microphone with respect to the distance to the closest wall.

In principle, the geometry of the microphone array may be arbitrary, aslong as the microphones are located in known positions. However, tosimplify computations, it is advantageous if the array is symmetricalaround the loudspeaker.

To detect vertical surfaces (walls) the loudspeaker and microphone arraymay preferably be arranged in one single plane, perpendicular to thewalls. A rotationally symmetrical array in one plane is known as auniform circular array (UCA).

If detection of non-vertical surfaces (e.g. ceiling and floor) is alsointended, the microphone array should span a 3D space around theloudspeaker. In this case, a symmetrical array will have microphoneslocated on the surface of a sphere with the loudspeaker in its center.Such an array is referred to as a uniform spherical array,

The directivity model may be acquired by measuring a set of far-fieldimpulse responses of the loudspeaker in an anechoic environment at a setof angular positions. Again, the type of microphone used to make themeasurements is not critical for the measurement. Such measurements arerelatively easy to perform. Preferably, but not necessarily, the angularpositions measurements are uniformly distributed around the loudspeaker.The number of far-field responses may be larger than the number ofmicrophones in the microphone array by a given factor, preferably aninteger factor.

In a case where the loudspeaker includes several drivers, thedirectivity model may be a combined model, measured during simultaneousexcitation of all drivers. Alternatively, an individual directivitysubmodel may be determined for each driver, and then superimposed.

A directivity model modelling each driver individually has the advantagethat the estimation process may involve selectively exciting one orseveral drivers, and identifying walls for each such measurement.

In a preferred embodiment, the method includes eliminating a direct pathcontribution from each measurement signal, the direct path contributionbeing based on a known geometrical relationship between the loudspeakerand the respective microphone and representing the excitation signalreceived by each microphone without reflection from the walls.

Where the microphone array is a uniform circular array, the signal modelis preferably evaluated for candidate image source locations placed in apolar grid and expressed in polar coordinates, in order to simplifycalculations. Where the microphone array is a uniform spherical array,spherical coordinates may be used.

In these cases, the dimensions of variable h are advantageouslyexpressed as radial and angular coordinates, and the convolution withthe multichannel filter may be performed as a product of (two or three)convolutions, including a linear convolution over the radial coordinate,and a circular convolution over each angular coordinate.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be described in more detail with reference tothe appended drawings, showing currently preferred embodiments of theinvention.

FIG. 1 is a block diagram of an approach for room estimation accordingto prior art.

FIG. 2 is a top view of a system according to an embodiment of theinvention.

FIG. 3 shows the system in FIG. 1 and image sources representing firstand second order reflections in a rectangular room.

FIG. 4 shows discretization of image source locations in a polarcoordinate model.

FIG. 5 shows a plane wave approaching a uniform circular microphonearray.

FIG. 6 shows examples of plane wave microphone array responses.

FIGS. 7 a and 7 b shows a complete signal model for two examplesdirections of arrival.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Systems and methods disclosed in the following may be implemented assoftware, firmware, hardware or a combination thereof. In a hardwareimplementation, the division of tasks does not necessarily correspond tothe division into physical units; to the contrary, one physicalcomponent may have multiple functionalities, and one task may be carriedout by several physical components in cooperation. Certain components orall components may be implemented as software executed by a digitalsignal processor or microprocessor, or be implemented as hardware or asan application-specific integrated circuit. Such software may bedistributed on computer readable media, which may comprise computerstorage media (or non-transitory media) and communication media (ortransitory media). As is well known to a person skilled in the art, theterm computer storage media includes both volatile and non-volatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by a computer. Further, it is well known to the skilledperson that communication media typically embodies computer readableinstructions, data structures, program modules or other data in amodulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media.

System setup FIGS. 2-3 show a system 1 that has at least one loudspeaker2 and a compact microphone array 3 with N microphones 4 arranged in aknown geometry surrounding the loudspeaker 2. The system 1 is placed ina space, here a rectangular room with four walls 5. The array iscompact, meaning that the extension r of the array is significantly,e.g. 5 times, smaller than the distance d to the surrounding walls 5(r«d). (Note that the figures are not to scale, and exaggerate the sizeof the microphone array.)

In the illustrated case, the array 3 is a uniform circular array (UCA)with radius r, and the loudspeaker is placed in the center of the array.The plane of the array is perpendicular to the walls, i.e. horizontal inmost cases. The illustrated geometry is not required for the generalprinciple of the invention, but will simplify calculations. Inparticular, if also other surfaces, such as the ceiling, should beestimated, then the microphone array should preferably extend in 3D(i.e. not in one plane).

The system is connected to a transceiver 6, configured to transmit atransmit signal to the loudspeaker, and to receive return signals fromthe microphones 4 in the array 3. The transmitter and receiver sides ofthe transceiver 6 are synchronized and coincide geometrically. Thetransceiver 6 is connected to processing circuitry 7 configured toestimate the geometry of the room based on sound signals emitted by theloudspeaker 2 and reflected by the walls (and other flat surfaces) ofthe room.

The transceiver 6 is configured to actively probe the room by generatinga known excitation signal x, which is applied to the loudspeaker 2 andemitted into the room. The acoustic waves emitted by the loudspeaker arehere modeled as sound rays, which are specularly reflected in the wallsof the room (and other surfaces). The loudspeaker 2 (and center of themicrophone array 3) form the origin of a polar coordinate system, whereθ=0 corresponds with the main emission direction of the loudspeaker 2.For first order reflections (i.e. sound rays emitted by the loudspeakerand reflected in the opposite direction) the wall is orthogonal to thesound ray in the reflection point 8. For practical purposes, it is hereassumed that the wall extends linearly with similar acousticcharacteristics.

Similar to optical reflections, a reflection from a wall can be regardedas a signal emitted from an “image source” located beyond the wall. InFIG. 3 , two first order image sources 9 and one second order imagesources 10 are illustrated. By determining the positions of these imagesources, the position (distance and orientation) of the walls can bedetermined. Specifically, the distance to a wall is half the distance toa first order image source, and the surface of the wall is perpendicularto the direction of arrival (DOA) from that image source.

By identifying and locating the image sources in the room, the walls(and potentially other reflecting surfaces) may be identified. In thefollowing, a signal model will be described, which provides arelationship between the image sources and the measured microphonesignals. The forward model treats the image sources as input, anddefines the microphone signals as the result of the system (inputsignal, loudspeaker, microphone array) acting on this input. The imagesources are identified by solving the reverse problem, i.e. determiningthe image sources when the microphone signals are known.

Candidate Image Source Locations

In polar coordinates, an image source location is {R_(s), θ_(s)}, whereR_(S) is the distance and θ_(S) is the direction of arrival. Inprinciple, an image source may be located anywhere in the room, and eachcoordinate is continuous. In order to facilitate solving the inverseproblem, it is beneficial to discretize distance R_(S) and angle θ_(S)and to define a grid of candidate locations. The general idea is tocreate a large dictionary of Rotated Image Source Impulse Responses(RISIR). The inverse problem is then solved by fitting a sparse numberof these RISIR in the dictionary, with the microphone observations. Oncethe RISIR that are likely to be in the measured signal are estimated,they can be mapped back to wall locations.

The dictionary can be computed efficiently for candidate locations on auniform polar grid. By exploiting the symmetry of the uniform circulararray, the image source responses can be evaluated for many directionsof arrival efficiently. As a consequence, instead of iterating over aset of (R_(i), θ_(i)), an input signal h is defined. The length of h isequal to the number of discrete points on the grid, and h contains thegains for all candidate image sources. The index of the nonzero valuesin h will then correspond to image source locations, and the value of hin these locations will be the respective image source gain.

Consider the set

that contains the location of S first and second order virtual sources,that dominate the early part of the room impulse response. A signalmodel of the room influence that only accounts for these S reflectionscan be parameterized by S locations (in two dimensions) of thecorresponding image sources.

In polar coordinates, the location of source s can be expressed asr_(s)=[R,θ]^(T), for R∈[r,R_(max)] and θ∈[0,2π], where r as above is theradius of the compact uniform circular array, so that

={r_(i)}_(i=0) ^(S-1). The same information can also be expressed as avector.

If the microphone measurements y(n,k) and excitation signal x(n) havebeen sampled in time with f_(s), then R can be discretized with steps ofΔR=c/f_(s). By dividing by the speed of sound, c, the total number ofdiscrete steps is T=R_(max)f_(s)/c. The angle θ can be discretized insteps 2π/NP, where Nis the number of microphones and P is a naturalnumber that determines an up-sampling factor (in order to have higherresolution than provided by the number of microphones). Thus we have atotal of NPT candidate locations for which we can compute themeasurement model. An example of a polar grid with NP=16 and T=50 isshown in FIG. 4 .

The discrete signal h is now defined as containing all the NPT weightsfor each of these image sources. The representation of the set {H} ismapped to a two dimensional discrete signal h(n, p), where n=0, . . . ,T−1 is proportional to the image source distance (and delay) and p=0, .. . , NP−1 is proportional to the direction of arrival (DOA). Aspreviously noted, the index of the nonzero values in h(n, p) correspondto the distance and DOA of the image sources.

$\begin{matrix}{{h\left( {n,p} \right)} = {\frac{1}{R}{\delta\left( {n - \left\lceil {\frac{R}{R_{{ma}x}}T} \right\rfloor} \right)}{\delta\left( {p - \left\lceil {\frac{\theta}{2\pi}{NP}} \right\rfloor} \right)}}} & \left( {{Eq}.1} \right)\end{matrix}$

Since the measurement model is preferably expressed using matrix-vectorproducts, it is convenient to define a vector containing the elements ofh(n, p). This is defined as follows

${h_{p} = {\left\lbrack {{h\left( {0,p} \right)},{h\left( {1,p} \right)},\ldots,{h\left( {{T - 1},p} \right)}} \right\rbrack^{T} \in {\mathbb{R}}^{T}}}{h = {\begin{bmatrix}h_{0} \\h_{1} \\ \vdots \\h_{{NP} - 1}\end{bmatrix} \in {\mathbb{R}}^{TNP}}}$

The choice for stacking the p=0 responses first, rather than the n=0 isarbitrary. However, the equations in the present description follow thisconvention.

Observe the relationship between the number of image sources S and thevector h as ∥h∥₀=S, where ∥·∥₀ denotes the l₀ norm. As mentioned above,for a rectangular room, the number of first and second order reflectionsis S=8. The input vector h is sparse, since in general we have thatS«NPT.

Microphone Array Plane Wave Response

The plane wave response of a compact microphone array (array response)assumes that the source signal is in the far field. Therefore, theattenuation of the signal is approximately equal for all microphones inthe array. The only difference between signals received by differentmicrophones will be a relative delay determined by the array geometry.In general such relative delays Δd_(i)(θ) are only a function ofdirection of arrival θ.

For a uniform circular array (like the array 3 in FIG. 2 ) the symmetryin θ can be exploited to compute the array response for many imagesources on a uniform grid using Fast Fourier Transform.

Consider a uniform circular array (UCA) consisting of N microphones. Themicrophone locations are denoted by {r, θ_(m)} where θ_(m)=2πi/N, i=0 .. . N−1. This is illustrated in FIG. 3 .

Consider now a single image source, whose location is {R_(s), θ_(s)}.The distance from each microphone to the source is then given by;

$\begin{matrix}{{d\left( {R_{s},\theta_{s},r,i} \right)} = \sqrt{R_{s}^{2} + r^{2} - {2R_{s}r{\cos\left( {\theta_{s} - \frac{2\pi i}{N}} \right)}}}} & \left( {{Eq}.2} \right)\end{matrix}$

The distance d may also be expressed as R_(s)+(Δd_(i)−r), where Δd_(i)is the relative distance for microphone i, so that Δd_(i)=d_(i)−R_(s)+r.This Δd_(i) will explain a plane-wave event on the microphone array,which will only depend on the direction of arrival from the source,θ_(s).

As the array is compact (source is in the far-field), we have Rs»r, and:

${\lim\limits_{R_{s}\rightarrow\infty}{\Delta{d\left( {R_{s},\theta_{s},i} \right)}}} = {{{\lim\limits_{R_{s}\rightarrow\infty}\sqrt{R_{s}^{2} + r^{2} - {2R_{s}r{\cos\left( {\theta_{s} - \frac{2\pi i}{N}} \right)}}}} - R_{s} + r} = {r\left( {1 - {\cos\left( {\theta_{s} - \frac{2\pi i}{N}} \right)}} \right)}}$

By dividing by the speed of sound, c, the relative measured delays for aplane wave arriving from θ_(s) can be expressed as a function only ofθ_(s):

$\begin{matrix}{{\Delta{\tau_{i}\left( {\theta_{s},r_{i}} \right)}} = {\frac{r}{c}\left( {1 - {\cos\left( {\theta_{s} - \frac{2\pi i}{N}} \right)}} \right)}} & \left( {{Eq}.3} \right)\end{matrix}$

The maximum relative delay between two microphones is bounded by 2r/c.If the sampling rate is f_(s), then the maximum length of a discretefinite impulse response filter that captures the differences in delayhas

$W = \left\lceil \frac{2{rf}_{s}}{c} \right\rceil$taps.

The microphone measurements y(n, k) can be interpreted as a twodimensional sampled signal, where n samples in time and k samples in themicrophone dimension. This microphone dimension is uniformly sampled. Acloser look at Eq. 3 from the perspective of the j:th microphone sample,shows that this is only a function of the difference

$\theta_{s} - {\frac{2\pi j}{N}.}$Therefore, if we wish to use the convolution theorem, we must evaluateθ_(s) with uniform intervals. This creates a shift-invariant steeringfunction that only depends on the difference between the i:th microphoneand the source angle.

A template mask matrix M is now defined.

$\begin{matrix}{m_{n,p} = \left\{ {{\begin{matrix}1 & {{{if}n} = \left\lceil {f_{s}\frac{r}{c}\left( {1 - {\cos\left( \frac{2\pi p}{NP} \right)}} \right)} \right\rceil} \\0 & {elsewhere}\end{matrix}{\forall n}},p} \right.} & \left( {{Eq}.4} \right)\end{matrix}$

It is noted that although there are N microphones, the template mask mayhave a factor P higher resolution, making it possible to have NPcandidate angle locations. A two-dimensional circular convolution with hand m, will now explain the plane wave for NP microphone channels.

As one can observe, the matrix M is essentially a delay and sum filterbank that is steered in θ_(s)=0. However, by circularly permutating thecolumns of M it is possible to steer into NP directions (in uniformsteps).

By using the far-field assumption, Δτ has been constructed in such a waythat it is independent of source distance R_(S). As a result, thetwo-dimensional convolution with M can be computed as the product of twoconvolutions. One convolution is delay (temporal translation) that isproportional to the source distance. The second convolution permutes themask, such that any plane wave direction can be modeled.

Specifically, one can now write a circular convolution in the microphoneindex dimension and a linear convolution in the microphone timedimension as a product of two convolutions:

$\begin{matrix}{{f\left( {t_{1},j} \right)}\overset{\Delta}{=}{\sum\limits_{\alpha = 0}^{{NP} - 1}{\sum\limits_{d = 0}^{T - 1}{{h\left( {d,\alpha} \right)}m_{{t_{1} - d},{\lbrack{j - \alpha}\rbrack}_{{mod}{NP}}}}}}} & \left( {{Eq}.5} \right)\end{matrix}$

Observe that f is now defined for NP microphone channels. In Eq. 5 thereis a circular convolution in the discrete microphone index dimension jand a linear convolution in the microphone time dimension t₁ Aphysically motivated interpretation of this convolution is that it mapssource directions of arrival to the microphone index dimension, which inour case is an uniform circular array. In other words it maps (sourcedirection of arrival×source distance) on (microphone channel×time). FIG.4 shows three examples of the mask M for different number ofmicrophones.

Loudspeaker Modelling

A loudspeaker is (typically) an electroacoustic device that connects therealm of electronics with the world of sound. A loudspeaker can convertan electric signal into pressure changes in the air around it. Often ofmuch interest for acousticians is the frequency response of aloudspeaker. The rule of thumb is that to reproduce music one needs thefull auditory band of 20 Hz-20 kHz or even higher.

Measuring the loudspeaker impulse response can be done by placing theloudspeaker under test in an anechoic room. A wide band excitationsignal is emitted and the response is measured with a microphone. Theloudspeaker is assumed to be a linear time-invariant system, whoseimpulse response is causal and finite. The estimate is computed bydeconvolving the excitation signal from the microphone measurements. Thedeconvolution may be computed by taking the inverse of a Toeplitzmatrix.

When attempting to localize reflections (image sources), using aloudspeaker model is useful for predicting the contribution of areflector at a particular location, on the measured microphone signal.The more precise the loudspeaker model, the better is the prediction andthus the inverse problem of estimating the locations given measurementsare also improved. One straightforward model is a measured loudspeakerimpulse response. However, a loudspeaker impulse response is notconstant in each transmitted direction. Indeed, it has been shown thatthe magnitude frequency response bandwidth is maximum directly in frontof the loudspeaker cone, and is reduced at the back of the loudspeaker.Put differently, wide angular sound coverage (off-axis response) isreduced at the back, since high frequency sound tends to leave thespeaker in narrow beams

Based on this, the loudspeaker impulse response ν(n) is a function oflistening position ν(n, r), in other words of the direction oftransmission and distance. Furthermore, by our own construction, theloudspeaker response ν(n) does not include the propagation delay.Therefore, if the distance is sufficiently large, such that thefar-field assumption holds, the loudspeaker can be modelled as afunction of only the direction of transmission. It should be noted thatthe far-field distance is proportional to the wavelength. As a result,in broadband scenarios, the far-field assumption may not hold for thelower frequencies. Therefore, if the far-field can be considered tostart at distance r₀ it is sufficient to model the loudspeaker impulseresponse at any position in the room further away than r₀.

Here, the loudspeaker model is given by a two dimensional ν(n, p), forn=0, . . . , K−1 and p=0, . . . , NP−1, where K is the length (number oftime samples) of each impulse response, and NP is the number of uniformdirections of transmission (NP is an integer which will be explainedbelow). The model ν(n, p) is determined by a series of NP measurements(one for each angle of transmission) in an anechoic chamber, wherein theemitted excitation signal is deconvolved from the measured signals. Themicrophone that picks up the measurement signals is placed at asufficient distance so as to ensure far-field conditions.

Complete Signal Model Φ

By combining the conclusions above, a complete linear measurement modelcan be formulated which maps an input signal representing the imagesources to microphone measurements:

$\begin{matrix}{{y\left( {i,j} \right)} = {\sum\limits_{t_{1} = 0}^{L - 1}{{x\left( {i - t_{1}} \right)}\left\lbrack {{a^{dp}\left( t_{1} \right)} + {\sum\limits_{\alpha = 0}^{{NP} - 1}{\sum\limits_{d = 0}^{T - 1}{{m\left( {{t_{1} - d},\left\lbrack {{jP} - \alpha} \right\rbrack_{{mod}{NP}}} \right)}{\sum\limits_{t_{2} = 0}^{K - 1}{{v\left( {{d - t_{2}},\alpha} \right)}{h\left( {t_{2},\alpha} \right)}}}}}}} \right\rbrack}}} & \left( {{Eq}.6} \right)\end{matrix}$

where:

y(n, k) is the microphone measurements

h(n, p) is the discrete input signal, i.e. potential image sourceslocations. The values of h are image source gain. The locations of theidentified image sources are indicated by the indices of nonzero entriesof h(n, p). As mentioned above, the knowledge that there are fewnon-zero values of h is used when solving the inverse problem.

ν(u, s) is the loudspeaker directivity model described above.

m(t, n) is the microphone array plane wave response as described above.

x(t) is the known excitation signal, and

a^(dp) is the impulse response of the direct path between theloudspeaker and microphone j.

It is noted that x, a^(dp), m(t, n) and ν(u, s) are all constant.

The model above performs a two dimensional convolution on input signalh(n, p). In particular since the first dimension of h denotes thedistance R_(S), convolving in the time dimension will alter the delay ofthe rotated image source response. Secondly, a circular convolution inthe second dimension of h, will permuted the microphone channels, suchthat any plane wave arriving from

$\theta_{r} = \frac{2\pi i}{NP}$can be modeled.

FIGS. 6 a-b show modelling of a single image source. As one can see, thechannel is composed in three sequential steps: The loudspeaker impulseresponse is added, the microphone plane wave response is added andfinally (not shown in the figure) the direct path is added and isconvolved with x(n). The key observations are that i) as the candidatelocation moves further away from the system, the signal is translated inthe time dimension and ii) if the source circles around the system thenthe loudspeaker impulse response changes and the array template maskpermutes circularly.

Solving the Problem

It can be shown that the signal model can be reformulated in terms ofmatrix-vector multiplication:y=Φh

where, as above, y denotes the N microphone measurements, h denotes thegains generated by all possible image sources in a room, and Φ is amatrix corresponding to the convolutions in Eq. 6.

The least square estimate of h can be found by minimizing |y−Φh|².Typically, this will be numerically challenging, considering thepotentially large number of solutions. However, if it can be assumedthat the vector h is sparse, i.e. that there are very few image sources,the problem will be simplified:

$\begin{matrix}\begin{matrix}\underset{h}{minimize} & {{y - {\Phi h}}}_{2}^{2} \\{{subject}{to}} & {{h}_{0} \leq {S.}}\end{matrix} & \left( {{Eq}.7} \right)\end{matrix}$

where S is the maximum number of expected image sources.

As mentioned above, considering only vertical walls in a shoe-box shapedroom, there will only be (at most) eight first and second order imagesources, and in this simple case h will thus have only have eight (orfewer) non-zero elements (S=8).

Unfortunately, solving eq. 7 leads to a non-convex optimization problem.In order to overcome this, the problem is relaxed to the

₁ norm, and the estimator ĥ_(sparse) can be found by solving:

$\begin{matrix}\begin{matrix}\underset{h}{minimize} & {{y - {\Phi h}}}_{2}^{2} \\{{subject}{to}} & {{h}_{1} \leq {\beta.}}\end{matrix} & \left( {{Eq}.8} \right)\end{matrix}$

It should be noted that eq. 8 is in the standard Lasso formulation.However, most solvers consider the so-called Lagrangian form:

$\begin{matrix}{{\hat{h}}_{sparse} = {{\underset{h}{argmin}{{y - {\Phi h}}}_{2}} + {\lambda{{h}_{1}.}}}} & \left( {{Eq}.9} \right)\end{matrix}$

where the exact relationship between λ and β is data dependent.

The standard Lasso problem works well when the received echo power fromeach wall of interest is approximately equal. However, if theloudspeaker is placed in a corner of the room, it is expected that theclose echoes have higher power compared to the distant echoes. This isaccounted for in the signal model by the gains in h. A second problem isthat, for many loudspeakers, the total loudspeaker impulse responseenergy varies with the angle of transmission. This influences the signalto noise ratio of the detection problem. It is expected that an echofrom a nearby wall, in the on-axis direction of the loudspeaker has amuch higher influence on the microphone measurements compared to a wallfacing the back of the loudspeaker that is placed further away.

The expected value for h is thus dependent on the distance of the walland the DOA and the energy in y decays over time. Both influences can becompensated for by having a weighted least squared and a weighted

₁ norm. Let Λ_(ls) denote a diagonal weighting matrix for the totalleast squares and let Λ_(h) denote a diagonal weighting matrix for thegain on the candidate locations. The general optimization problem isthen given by:

$\begin{matrix}{{{\hat{h}}_{sparse} = {{{\underset{h}{\arg\min}\left( {y - {\Phi h}} \right)}^{\dagger}{\Lambda_{ls}\left( {y - {\Phi h}} \right)}} + {{\Lambda_{h}h}}_{1}}},} & {{Eq}.10}\end{matrix}$

where † is the Hermitian operator. After solving, the non-zero elementsof ĥ_(sparse) will be the gain of image sources, and the indices ofthese elements will represent the locations of these image sources.

As explained above, determining the distance to and orientation of thewalls is trivial based on the image source locations.

In its broadest form, the present invention relates to a method forestimating an acoustic influence of walls of a room, comprising emittinga known excitation sound signal, receiving a set of measurement signals,each measurement signal being received by one microphone in a microphonearray and each measurement signal including a set of echoes caused byreflections by the walls, solving a linear system of equations toidentify locations of image source and estimating the acoustic influencebased these image sources. The signal model includes a convolution of:

-   -   the excitation signal,    -   a multichannel filter (M) representing the relative delays of        the microphones in the microphone array, the relative delays        determined based on a known geometry of the microphone array,        and    -   a directivity model ν(n, p) of the driver(s) in the form of an        anechoic far-field impulse response as a function of transmit        angle.

The person skilled in the art realizes that the present invention by nomeans is limited to the preferred embodiments described above. On thecontrary, many modifications and variations are possible within thescope of the appended claims. For example, the details of the componentsin the system may vary.

What is claimed is:
 1. A method for estimating an acoustic influence ofwalls of a room, using a system (1) including: a loudspeaker (2)including at least one acoustic driver, and a compact microphone array(3) including a set of microphones (4) arranged in a known geometryaround the loudspeaker, the method comprising: emitting a knownexcitation sound signal, receiving a set of measurement signals, theeach measurement signal being received by one microphone in themicrophone array and the each measurement signal including a direct pathcomponent and a set of echoes, said echoes caused by reflections by saidwalls, defining a linear system of equations y=Φh, wherein y is the setof measurement signals, Φ is a signal model of the system, and h is avector, each element of h representing a candidate location of an imagesource with a value representing a gain of said image source,identifying non-zero values of h by using least square estimation tominimize |y−Φh|, wherein the least-squares estimation is l₁-regularizedin order to restrict the number of non-zero values of h, and estimatingthe acoustic influence based on image sources corresponding to saididentified non-zero values of h, characterized in that said signal modelincludes a convolution of: the known excitation signal (x), amultichannel filter (M) representing the relative delays of themicrophones in the microphone array, said relative delays determinedbased on a known geometry of the microphone array, and a directivitymodel of the at least one acoustic driver in the form of an anechoicfar-field impulse response of the at least one acoustic driver as afunction of transmit angle, wherein the anechoic far-field impulseresponse of the at least one acoustic driver is measured in an anechoicenvironment using at least one measurement microphone arranged in thefar-field of the at least one acoustic driver.
 2. The method accordingto claim 1, wherein said directivity model is acquired by measuring aset of far-field impulse responses of the loudspeaker in an anechoicenvironment at a set of angular positions.
 3. The method according toclaim 2, wherein said angular positions are uniformly distributed. 4.The method according to claim 2, wherein said set of far-field impulseresponses includes NP angular positions in said plane.
 5. The methodaccording to claim 2, wherein the loudspeaker has more than one acousticdriver, and wherein said directivity model is acquired by activating allthe acoustic drivers simultaneously.
 6. The method according to claim 2,wherein the loudspeaker has more than one acoustic driver, and whereinsaid directivity model includes several submodels, each acquired byactivating one acoustic driver at a time.
 7. The method according toclaim 1, further comprising eliminating a direct path contribution fromthe each measurement signal, said direct path contribution being basedon a known geometrical relationship between the loudspeaker and therespective microphone and representing the known excitation signalreceived by each microphone without reflection from the walls.
 8. Themethod according to claim 1, wherein the microphone array is symmetricalaround the loudspeaker.
 9. The method according to claim 8, wherein themicrophone array is a uniform spherical array.
 10. The method accordingto claim 9, wherein the signal model is evaluated for candidate imagesource locations placed in a spherical grid, said locations expressed inspherical coordinates including a radial coordinate and two angularcoordinates.
 11. The method according to claim 10, wherein thedimensions of h represent radial and angular coordinates.
 12. The methodaccording to claim 10, wherein the convolution with the multichannelfilter is performed as a product of convolutions, including a linearconvolution over the radial coordinate, and a circular convolution overeach angular coordinate.
 13. The method according to claim 1, whereinthe loudspeaker and the microphone array are arranged in a single plane,substantially perpendicular to the vertical walls of the room.
 14. Themethod according to claim 13, wherein the microphone array is a uniformcircular array.
 15. The method according to claim 14, wherein the signalmodel is evaluated for candidate image source locations placed in apolar grid in said plane, said locations expressed in polar coordinatesincluding a radial coordinate and an angular coordinate.
 16. The methodaccording to claim 1, wherein the known excitation signal is anexponential sine sweep.
 17. A method for estimating an acousticinfluence of walls of a room, using a system (1) including: aloudspeaker (2) including at least one acoustic driver, and a compactmicrophone array (3) including a set of microphones (4) arranged in aknown geometry around the loudspeaker, the method comprising: emitting aknown excitation sound signal, receiving a set of measurement signals,the each measurement signal being received by one microphone in themicrophone array and the each measurement signal including a direct pathcomponent and a set of echoes, said echoes caused by reflections by saidwalls, defining a linear system of equations y=Φh, wherein y is the setof measurement signals, Φ is a signal model of the system, and h is avector, each element of h representing a candidate location of an imagesource with a value representing a gain of said image source,identifying non-zero values of h by using least square estimation tominimize |y−Φh| wherein the least-squares estimation is l₁-regularizedin order to restrict the number of non-zero values of h, and estimatingthe acoustic influence based on image sources corresponding to saididentified non-zero values of h, characterized in that said signal modelincludes a convolution of: the known excitation signal (x), amultichannel filter (M) representing the relative delays of themicrophones in the microphone array, said relative delays determinedbased on a known geometry of the microphone array, and a directivitymodel of the at least one acoustic driver in the form of an anechoicfar-field impulse response as a function of transmit angle; wherein theloudspeaker and the microphone array are arranged in a single plane,substantially perpendicular to the vertical walls of the room; whereinthe microphone array is a uniform circular array; and wherein the signalmodel is evaluated for candidate image source locations placed in apolar grid in said plane, said locations expressed in polar coordinatesincluding a radial coordinate and an angular coordinate.
 18. The methodaccording to claim 17, wherein the dimensions of h represent radial andangular coordinates.
 19. The method according to claim 17, wherein theconvolution with the multichannel filter is performed as a product ofconvolutions, including a linear convolution over the radial coordinate,and a circular convolution over the angular coordinate.
 20. A method forestimating an acoustic influence of walls of a room, using a system (1)including: a loudspeaker (2) including at least one acoustic driver, anda compact microphone array (3) including a set of microphones (4)arranged in a known geometry around the loudspeaker, the methodcomprising: emitting a known excitation sound signal, receiving a set ofmeasurement signals, the each measurement signal being received by onemicrophone in the microphone array and the each measurement signalincluding a direct path component and a set of echoes, said echoescaused by reflections by said walls, defining a linear system ofequations y=Φh, wherein y is the set of measurement signals, Φ is asignal model of the system, and h is a vector, each element of hrepresenting a candidate location of an image source with a valuerepresenting a gain of said image source, identifying non-zero values ofh by using least square estimation to minimize |y−Φh| wherein theleast-squares estimation is l₁-regularized in order to restrict thenumber of non-zero values of h, and estimating the acoustic influencebased on image sources corresponding to said identified non-zero valuesof h, characterized in that said signal model includes a convolution of:the known excitation signal (x), a multichannel filter (M) representingthe relative delays of the microphones in the microphone array, saidrelative delays determined based on a known geometry of the microphonearray, and a directivity model of the at least one acoustic driver inthe form of an anechoic far-field impulse response as a function oftransmit angle; wherein the microphone array is symmetrical around theloudspeaker; wherein the microphone array is a uniform spherical array;wherein the signal model is evaluated for candidate image sourcelocations placed in a spherical grid, said locations expressed inspherical coordinates including a radial coordinate and two angularcoordinates; and wherein the convolution with the multichannel filter isperformed as a product of convolutions, including a linear convolutionover the radial coordinate, and a circular convolution over each angularcoordinate.